What Does it Mean for a Neural Network to Learn a "World Model"?
Artificial Intelligence
2025-07-30 v1 Computation and Language
Abstract
We propose a set of precise criteria for saying a neural net learns and uses a "world model." The goal is to give an operational meaning to terms that are often used informally, in order to provide a common language for experimental investigation. We focus specifically on the idea of representing a latent "state space" of the world, leaving modeling the effect of actions to future work. Our definition is based on ideas from the linear probing literature, and formalizes the notion of a computation that factors through a representation of the data generation process. An essential addition to the definition is a set of conditions to check that such a "world model" is not a trivial consequence of the neural net's data or task.
Cite
@article{arxiv.2507.21513,
title = {What Does it Mean for a Neural Network to Learn a "World Model"?},
author = {Kenneth Li and Fernanda Viégas and Martin Wattenberg},
journal= {arXiv preprint arXiv:2507.21513},
year = {2025}
}